Driver assistance systems, e.g., what are referred to as ACC systems (adaptive cruise control), which permit automatic control of distance to a preceding vehicle, or safety systems (PSS; predictive safety systems) require constantly up-to-date information about objects in the surround field of the vehicle, particularly about the locations and movements of other road users. This information is usually obtained with the aid of a locating device, e.g., with the aid of a radar sensor having angular resolution, by which the distances, relative velocities and azimuth angles of preceding vehicles are able to be tracked. From the distances and azimuth angles, it is then possible to calculate the corresponding lateral positions of the objects, as well.
The radar sensor operates in periodically successive measuring cycles, so that the driver assistance system receives an up-to-date record of location data at regular intervals, e.g., at intervals of 1 ms. Tracking is understood to be a procedure by which the objects located in an instantaneous measuring cycle are identified with the objects located in previous cycles, so that the dynamic behavior of the objects may be monitored over time. Suitable algorithms are known for that purpose, which are executed in an electronic data-processing system belonging to the driver assistance system.
However, the tracking is rendered difficult because occasionally occurring interferences lead to individual location data being corrupted or missing completely. In order to be able to continue tracking the objects in such cases as well, and to obtain sufficiently reliable information about the objects, it is known to predict the movements of the objects with the aid of a dynamic model which is based on plausible assumptions about the movement of the object. With the help of this model, missing or corrupted data may then also be replaced by plausible estimated values. For example, the modeling is accomplished with the aid of predictive tracking filters such as Kalman filters, for instance.
Such predictive tracking is particularly important in determining the lateral offset (the y-position) of the located objects based on the measured azimuth angles. The measurement of this lateral offset is particularly susceptible to interference and errors, because the customary locating devices have only a limited angular-resolution capability, and in addition, the error tolerances increase proportionally to the object distance.
A simple dynamic model by which, in particular, the determination of the lateral offset of objects may be improved, is based, for instance, on the assumption that the lateral offset in a vehicle-fixed sensor coordinate system remains constant. However, in many situations, this hypothesis is not correct, so that faulty or inaccurate object estimations come about.
In addition, what is referred to as a parallel-driving model has already been proposed, which has proven to be suitable for ACC systems, in particular. This model is based on the hypothesis that all objects (all preceding vehicles) and the host vehicle are constantly moving on a parallel, straight or curved course. This hypothesis is at least approximately correct for most functions relevant for distance control.
However, there are also situations in which the parallel-driving hypothesis is inapplicable, for instance, when the driver of the host vehicle undertakes a lane change. For an ACC system in which the intention is to regulate the distance to a vehicle directly preceding in one's own lane, this case is not relevant so long as the host vehicle remains in its lane. However, the situation is different in PSS systems, for example, in which, in the case of an immediately imminent crash, emergency measures are initiated automatically for preventing the crash, for instance, by a full brake application, or for mitigating the accident consequences. In assistance systems of this kind, a faulty or inaccurate object estimation as a result of incorrect model hypotheses may lead to unwanted erroneous activations.